integrate_fluxes.ipynb¶
This script loads the Zarr climate data from Amazon S3 into an xarray Dataset. Then, it generates a timeseries of the FLNT variable spatially averaged over the entire globe for each ensemble mean.
Data intake¶
First, we load a few of the libraries we will be using for data intake. s3fs is a library for accessing files on the Amazon S3 cloud. zarr is a python library that deals with highly-compressed and easily-accessed files. xarray is a labeled dataset format commonly used to handle multidimensional geospatial data. xarray is built around the dask framework, which is a library for parallelizing computation in python. We will be using dask.distributed to make the multiple cores within our remote machine behave like multi-computer cluster.
Execute the following cell by pressing SHIFT+RETURN. I recommend looking up a list of Jupyter commands like the one found here.
In [4]:
import s3fs
import zarr
import xarray as xr
import dask
from dask.distributed import Client
import numpy as np
import geoviews as gv
import cftime
import pandas as pd
import holoviews as hv
gv.extension('bokeh')
First, we'll set up dask parallelization on this computer. After running the cell below, you should be able to go to localhost:8888 to view a nifty distributed dashboard, assuming you have configured port 8888 for forwarding over ssh.
In [2]:
client = Client()
client
Out[2]:
Client
|
Cluster
|
With parallelization set up, we're ready to load the data. I already uploaded Zarr datastores of every simulation case to the data-viz-server S3 bucket. This data contains all of the variables and metadata from the original NetCDF data, but in Zarr format. When we run the code below, the cases dictionary will be populated with xarray datasets fetched from the cloud. This code will execute relatively quickly (~15 s) because only the metadata is fetched -- the variable data itself is only downloaded as needed.
In [3]:
case_paths = {
"GLOBAL":"cobalt.GLOBAL02_CAM_ZARR/",
"GLOBAL_MAY_NAO":"cobalt.GLOBAL02.MAY.NAO_CAM_ZARR/",
"GLOBAL_MAY_PAO":"cobalt.GLOBAL02.MAY.PAO_CAM_ZARR/",
"CONTROL":"CONTROL03_CAM_ZARR/",
"CONTROL_MAY_NAO":"CONTROL03.MAY.NAO_CAM_ZARR/",
"CONTROL_MAY_PAO":"CONTROL03.MAY.PAO_CAM_ZARR/",
"FRAM_MAY":"cobalt.FRAM.MAY_CAM_ZARR/",
"FRAM_MAY_NAO":"cobalt.FRAM.MAY.NAO_CAM_ZARR/",
"FRAM_MAY_PAO":"cobalt.FRAM.MAY.PAO_CAM_ZARR/"
}
cases = {}
# Initialize the S3 file system
s3 = s3fs.S3FileSystem()
for k,v in case_paths.items():
s3_path = 's3://data-viz-server/{}'.format(v)
store = s3fs.S3Map(root=s3_path, s3=s3, check=False)
# Read Zarr file and put into cases dictionary
cases[k] = xr.open_zarr(store=store, consolidated=True)
cases # display 'cases', the dictionary of xarray Datasets.
Out[3]:
{'GLOBAL': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 1033)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2086-06-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(1033,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(1033,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(1033, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.GLOBAL02
host: n716022
initial_file: f09_g16.B.04.EXT.cam.i.2077-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'GLOBAL_MAY_NAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 969)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2081-02-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(969,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(969, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.GLOBAL02.MAY.NAO
host: n716016
initial_file: f09_g16.B.04.EXT.cam.i.2068-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'GLOBAL_MAY_PAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 969)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2081-02-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(969,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(969, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.GLOBAL02.MAY.PAO
host: n716016
initial_file: f09_g16.B.04.EXT.cam.i.2066-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'CONTROL': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 977)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-02-01 00:00:00 ... 2081-06-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(977,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(977,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(977, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.CONTROL03
host: n716024
initial_file: f09_g16.B.04.EXT.cam.i.2075-01-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /d/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'CONTROL_MAY_NAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 984)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-02-01 00:00:00 ... 2082-01-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(984,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(984,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(984, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.CONTROL03.MAY.NAO
host: n716027
initial_file: f09_g16.B.04.EXT.cam.i.2068-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'CONTROL_MAY_PAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 973)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-02-01 00:00:00 ... 2081-02-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(973,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(973,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(973, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.CONTROL03.MAY.PAO
host: n716031
initial_file: f09_g16.B.04.EXT.cam.i.2066-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'FRAM_MAY': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 974)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2081-07-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(974,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(974,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(974, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.FRAM.MAY
host: n716027
initial_file: f09_g16.B.04.EXT.cam.i.2077-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /d/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'FRAM_MAY_NAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 969)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2081-02-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(969,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(969, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.FRAM.MAY.NAO
host: n716005
initial_file: f09_g16.B.04.EXT.cam.i.2068-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...,
'FRAM_MAY_PAO': <xarray.Dataset>
Dimensions: (ilev: 27, lat: 192, lev: 26, lon: 288, nbnd: 2, slat: 191, slon: 288, time: 969)
Coordinates:
* ilev (ilev) float64 2.194 4.895 9.882 18.05 ... 956.0 985.1 1e+03
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lev (lev) float64 3.545 7.389 13.97 23.94 ... 929.6 970.6 992.6
* lon (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
* slat (slat) float64 -89.53 -88.59 -87.64 ... 87.64 88.59 89.53
* slon (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
* time (time) object 2000-06-01 00:00:00 ... 2081-02-01 00:00:00
Dimensions without coordinates: nbnd
Data variables:
AEROD_v (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDHGH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLIQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CLDLOW (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDMED (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLDTOT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
CLOUD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
CONCLD (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DCQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTCOND (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
DTV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
EMIS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FICE (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
FLDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FLUTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSDTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSNTOAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
FSUTOA (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICEFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
ICIMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ICWMR (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
LANDFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
LWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OCNFRAC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
OMEGA (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
OMEGAT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
P0 (time) float64 dask.array<chunksize=(969,), meta=np.ndarray>
PBLH (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PHIS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSC (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PRECSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
PSL (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Q (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
QRL (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
QRS (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
RELHUM (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
SFCLDICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SFCLDLIQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SHFLX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHICE (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SNOWHLND (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SOLIN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
SWCF (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
T (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
TAUX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TAUY (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDCWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDIWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TGCLDLWP (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TMQ (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TREFHT (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TS (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMN (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
TSMX (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
U (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
U10 (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
UU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
V (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VD01 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VQ (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VT (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VU (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
VV (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
Z3 (time, lev, lat, lon) float32 dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
ch4vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
co2vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
date (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
date_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
datesec (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
f11vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
f12vmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
gw (time, lat) float64 dask.array<chunksize=(1, 192), meta=np.ndarray>
hyai (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hyam (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
hybi (time, ilev) float64 dask.array<chunksize=(1, 27), meta=np.ndarray>
hybm (time, lev) float64 dask.array<chunksize=(1, 26), meta=np.ndarray>
mdt (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
n2ovmr (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
nbdate (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nbsec (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ndcur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nlon (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
nsbase (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
nscur (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
nsteph (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>
ntrk (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrm (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
ntrn (time) int32 dask.array<chunksize=(969,), meta=np.ndarray>
sol_tsi (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
time_bnds (time, nbnd) object dask.array<chunksize=(969, 2), meta=np.ndarray>
time_written (time) |S8 dask.array<chunksize=(1,), meta=np.ndarray>
w_stag (time, slat) float64 dask.array<chunksize=(1, 191), meta=np.ndarray>
wnummax (time, lat) int32 dask.array<chunksize=(1, 192), meta=np.ndarray>
Attributes:
Conventions: CF-1.0
Version: $Name$
case: f09_g16.B.cobalt.FRAM.MAY.PAO
host: n716008
initial_file: f09_g16.B.04.EXT.cam.i.2066-05-01-00000.nc
logname: clmfrm01
revision_Id: $Id$
source: CAM
title: UNSET
topography_file: /e/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_rem...}
Let's examine one of the datasets in more detail. Like in MATLAB, in a Jupyter notebook you can simply state a variable and it will be printed. Let's do that with the Control(neutral AO) case to examine its Dataset in more detail.
In [9]:
cases["CONTROL"]
Out[9]:
xarray.Dataset
- ilev: 27
- lat: 192
- lev: 26
- lon: 288
- nbnd: 2
- slat: 191
- slon: 288
- time: 977
- ilev(ilev)float642.194 4.895 9.882 ... 985.1 1e+03
- formula_terms :
- a: hyai b: hybi p0: P0 ps: PS
- long_name :
- hybrid level at interfaces (1000*(A+B))
- positive :
- down
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- level
array([ 2.194067, 4.895209, 9.882418, 18.05201 , 29.83724 , 44.62334 , 61.60587 , 78.51243 , 92.3658 , 108.66359 , 127.83708 , 150.39371 , 176.93043 , 208.14944 , 244.87709 , 288.08522 , 338.91731 , 398.71865 , 469.0718 , 551.83871 , 649.20969 , 744.38289 , 831.02123 , 903.30029 , 955.99746 , 985.1122 , 1000. ]) - lat(lat)float64-90.0 -89.06 -88.12 ... 89.06 90.0
- long_name :
- latitude
- units :
- degrees_north
array([-90. , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958, -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508, -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058, -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607, -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157, -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707, -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257, -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806, -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356, -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906, -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455, -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005, -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555, -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105, -10.837696, -9.895288, -8.95288 , -8.010471, -7.068063, -6.125654, -5.183246, -4.240838, -3.298429, -2.356021, -1.413613, -0.471204, 0.471204, 1.413613, 2.356021, 3.298429, 4.240838, 5.183246, 6.125654, 7.068063, 8.010471, 8.95288 , 9.895288, 10.837696, 11.780105, 12.722513, 13.664921, 14.60733 , 15.549738, 16.492147, 17.434555, 18.376963, 19.319372, 20.26178 , 21.204188, 22.146597, 23.089005, 24.031414, 24.973822, 25.91623 , 26.858639, 27.801047, 28.743455, 29.685864, 30.628272, 31.570681, 32.513089, 33.455497, 34.397906, 35.340314, 36.282723, 37.225131, 38.167539, 39.109948, 40.052356, 40.994764, 41.937173, 42.879581, 43.82199 , 44.764398, 45.706806, 46.649215, 47.591623, 48.534031, 49.47644 , 50.418848, 51.361257, 52.303665, 53.246073, 54.188482, 55.13089 , 56.073298, 57.015707, 57.958115, 58.900524, 59.842932, 60.78534 , 61.727749, 62.670157, 63.612565, 64.554974, 65.497382, 66.439791, 67.382199, 68.324607, 69.267016, 70.209424, 71.151832, 72.094241, 73.036649, 73.979058, 74.921466, 75.863874, 76.806283, 77.748691, 78.691099, 79.633508, 80.575916, 81.518325, 82.460733, 83.403141, 84.34555 , 85.287958, 86.230366, 87.172775, 88.115183, 89.057592, 90. ]) - lev(lev)float643.545 7.389 13.97 ... 970.6 992.6
- formula_terms :
- a: hyam b: hybm p0: P0 ps: PS
- long_name :
- hybrid level at midpoints (1000*(A+B))
- positive :
- down
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- level
array([ 3.544638, 7.388814, 13.967214, 23.944625, 37.23029 , 53.114605, 70.05915 , 85.439115, 100.514695, 118.250335, 139.115395, 163.66207 , 192.539935, 226.513265, 266.481155, 313.501265, 368.81798 , 433.895225, 510.455255, 600.5242 , 696.79629 , 787.70206 , 867.16076 , 929.648875, 970.55483 , 992.5561 ]) - lon(lon)float640.0 1.25 2.5 ... 356.2 357.5 358.8
- long_name :
- longitude
- units :
- degrees_east
array([ 0. , 1.25, 2.5 , ..., 356.25, 357.5 , 358.75])
- slat(slat)float64-89.53 -88.59 ... 88.59 89.53
- long_name :
- staggered latitude
- units :
- degrees_north
array([-89.528796, -88.586387, -87.643979, -86.701571, -85.759162, -84.816754, -83.874346, -82.931937, -81.989529, -81.04712 , -80.104712, -79.162304, -78.219895, -77.277487, -76.335079, -75.39267 , -74.450262, -73.507853, -72.565445, -71.623037, -70.680628, -69.73822 , -68.795812, -67.853403, -66.910995, -65.968586, -65.026178, -64.08377 , -63.141361, -62.198953, -61.256545, -60.314136, -59.371728, -58.429319, -57.486911, -56.544503, -55.602094, -54.659686, -53.717277, -52.774869, -51.832461, -50.890052, -49.947644, -49.005236, -48.062827, -47.120419, -46.17801 , -45.235602, -44.293194, -43.350785, -42.408377, -41.465969, -40.52356 , -39.581152, -38.638743, -37.696335, -36.753927, -35.811518, -34.86911 , -33.926702, -32.984293, -32.041885, -31.099476, -30.157068, -29.21466 , -28.272251, -27.329843, -26.387435, -25.445026, -24.502618, -23.560209, -22.617801, -21.675393, -20.732984, -19.790576, -18.848168, -17.905759, -16.963351, -16.020942, -15.078534, -14.136126, -13.193717, -12.251309, -11.308901, -10.366492, -9.424084, -8.481675, -7.539267, -6.596859, -5.65445 , -4.712042, -3.769634, -2.827225, -1.884817, -0.942408, 0. , 0.942408, 1.884817, 2.827225, 3.769634, 4.712042, 5.65445 , 6.596859, 7.539267, 8.481675, 9.424084, 10.366492, 11.308901, 12.251309, 13.193717, 14.136126, 15.078534, 16.020942, 16.963351, 17.905759, 18.848168, 19.790576, 20.732984, 21.675393, 22.617801, 23.560209, 24.502618, 25.445026, 26.387435, 27.329843, 28.272251, 29.21466 , 30.157068, 31.099476, 32.041885, 32.984293, 33.926702, 34.86911 , 35.811518, 36.753927, 37.696335, 38.638743, 39.581152, 40.52356 , 41.465969, 42.408377, 43.350785, 44.293194, 45.235602, 46.17801 , 47.120419, 48.062827, 49.005236, 49.947644, 50.890052, 51.832461, 52.774869, 53.717277, 54.659686, 55.602094, 56.544503, 57.486911, 58.429319, 59.371728, 60.314136, 61.256545, 62.198953, 63.141361, 64.08377 , 65.026178, 65.968586, 66.910995, 67.853403, 68.795812, 69.73822 , 70.680628, 71.623037, 72.565445, 73.507853, 74.450262, 75.39267 , 76.335079, 77.277487, 78.219895, 79.162304, 80.104712, 81.04712 , 81.989529, 82.931937, 83.874346, 84.816754, 85.759162, 86.701571, 87.643979, 88.586387, 89.528796]) - slon(slon)float64-0.625 0.625 1.875 ... 356.9 358.1
- long_name :
- staggered longitude
- units :
- degrees_east
array([ -0.625, 0.625, 1.875, ..., 355.625, 356.875, 358.125])
- time(time)object2000-02-01 00:00:00 ... 2081-06-01 00:00:00
- bounds :
- time_bnds
- long_name :
- time
array([cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0), cftime.DatetimeNoLeap(2000, 3, 1, 0, 0, 0, 0), cftime.DatetimeNoLeap(2000, 4, 1, 0, 0, 0, 0), ..., cftime.DatetimeNoLeap(2081, 4, 1, 0, 0, 0, 0), cftime.DatetimeNoLeap(2081, 5, 1, 0, 0, 0, 0), cftime.DatetimeNoLeap(2081, 6, 1, 0, 0, 0, 0)], dtype=object)
- AEROD_v(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Total Aerosol Optical Depth in visible band
- units :
- 1
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDHGH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Vertically-integrated high cloud
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Grid box averaged cloud ice amount
- mdims :
- 1
- units :
- kg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDLIQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Grid box averaged cloud liquid amount
- mdims :
- 1
- units :
- kg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDLOW(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Vertically-integrated low cloud
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDMED(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Vertically-integrated mid-level cloud
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLDTOT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Vertically-integrated total cloud
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CLOUD(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Cloud fraction
- mdims :
- 1
- units :
- fraction
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - CONCLD(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Convective cloud cover
- mdims :
- 1
- units :
- fraction
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - DCQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Q tendency due to moist processes
- mdims :
- 1
- units :
- kg/kg/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - DTCOND(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- T tendency - moist processes
- mdims :
- 1
- units :
- K/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - DTV(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- T vertical diffusion
- mdims :
- 1
- units :
- K/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - EMIS(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- cloud emissivity
- mdims :
- 1
- units :
- 1
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FICE(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Fractional ice content within cloud
- mdims :
- 1
- units :
- fraction
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLDS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Downwelling longwave flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLDSC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky downwelling longwave flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLNS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Net longwave flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLNSC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky net longwave flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLNT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Net longwave flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLNTC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky net longwave flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLUT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Upwelling longwave flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FLUTC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky upwelling longwave flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSDS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Downwelling solar flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSDSC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky downwelling solar flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSDTOA(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Downwelling solar flux at top of atmosphere
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Net solar flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNSC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky net solar flux at surface
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Net solar flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNTC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky net solar flux at top of model
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNTOA(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Net solar flux at top of atmosphere
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSNTOAC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Clearsky net solar flux at top of atmosphere
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - FSUTOA(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Upwelling solar flux at top of atmosphere
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - ICEFRAC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Fraction of sfc area covered by sea-ice
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - ICIMR(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Prognostic in-cloud ice mixing ratio
- mdims :
- 1
- units :
- kg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - ICWMR(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Prognostic in-cloud water mixing ratio
- mdims :
- 1
- units :
- kg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - LANDFRAC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Fraction of sfc area covered by land
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - LHFLX(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Surface latent heat flux
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - LWCF(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Longwave cloud forcing
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - OCNFRAC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Fraction of sfc area covered by ocean
- units :
- fraction
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - OMEGA(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Vertical velocity (pressure)
- mdims :
- 1
- units :
- Pa/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - OMEGAT(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Vertical heat flux
- mdims :
- 1
- units :
- K Pa/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - P0(time)float64dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- reference pressure
- units :
- Pa
Array Chunk Bytes 7.82 kB 7.82 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - PBLH(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- PBL height
- units :
- m
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PHIS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- long_name :
- Surface geopotential
- units :
- m2/s2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PRECC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Convective precipitation rate (liq + ice)
- units :
- m/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PRECL(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Large-scale (stable) precipitation rate (liq + ice)
- units :
- m/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PRECSC(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Convective snow rate (water equivalent)
- units :
- m/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PRECSL(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Large-scale (stable) snow rate (water equivalent)
- units :
- m/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Surface pressure
- units :
- Pa
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - PSL(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Sea level pressure
- units :
- Pa
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - Q(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Specific humidity
- mdims :
- 1
- units :
- kg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - QFLX(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Surface water flux
- units :
- kg/m2/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - QREFHT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Reference height humidity
- units :
- kg/kg
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - QRL(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Longwave heating rate
- mdims :
- 1
- units :
- K/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - QRS(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Solar heating rate
- mdims :
- 1
- units :
- K/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - RELHUM(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Relative humidity
- mdims :
- 1
- units :
- percent
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SFCLDICE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- CLDICE surface flux
- units :
- kg/m2/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SFCLDLIQ(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- CLDLIQ surface flux
- units :
- kg/m2/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SHFLX(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Surface sensible heat flux
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SNOWHICE(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Snow depth over ice
- units :
- m
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SNOWHLND(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Water equivalent snow depth
- units :
- m
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SOLIN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Solar insolation
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - SWCF(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Shortwave cloud forcing
- units :
- W/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - T(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Temperature
- mdims :
- 1
- units :
- K
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TAUX(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Zonal surface stress
- units :
- N/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TAUY(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Meridional surface stress
- units :
- N/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TGCLDCWP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Total grid-box cloud water path (liquid and ice)
- units :
- gram/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TGCLDIWP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Total grid-box cloud ice water path
- units :
- gram/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TGCLDLWP(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- Sampling_Sequence :
- rad_lwsw
- cell_methods :
- time: mean
- long_name :
- Total grid-box cloud liquid water path
- units :
- gram/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TMQ(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Total (vertically integrated) precipitable water
- units :
- kg/m2
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TREFHT(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Reference height temperature
- units :
- K
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TS(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Surface temperature (radiative)
- units :
- K
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TSMN(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: minimum
- long_name :
- Minimum surface temperature over output period
- units :
- K
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - TSMX(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: maximum
- long_name :
- Maximum surface temperature over output period
- units :
- K
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - U(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Zonal wind
- mdims :
- 1
- units :
- m/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - U10(time, lat, lon)float32dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- 10m wind speed
- units :
- m/s
Array Chunk Bytes 216.10 MB 221.18 kB Shape (977, 192, 288) (1, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - UU(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Zonal velocity squared
- mdims :
- 1
- units :
- m2/s2
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - V(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Meridional wind
- mdims :
- 1
- units :
- m/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - VD01(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Vertical diffusion of Q
- mdims :
- 1
- units :
- kg/kg/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - VQ(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Meridional water transport
- mdims :
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- units :
- m/skg/kg
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - VT(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Meridional heat transport
- mdims :
- 1
- units :
- K m/s
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - VU(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
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- long_name :
- Meridional flux of zonal momentum
- mdims :
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- units :
- m2/s2
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - VV(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
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- long_name :
- Meridional velocity squared
- mdims :
- 1
- units :
- m2/s2
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - Z3(time, lev, lat, lon)float32dask.array<chunksize=(1, 26, 192, 288), meta=np.ndarray>
- cell_methods :
- time: mean
- long_name :
- Geopotential Height (above sea level)
- mdims :
- 1
- units :
- m
Array Chunk Bytes 5.62 GB 5.75 MB Shape (977, 26, 192, 288) (1, 26, 192, 288) Count 978 Tasks 977 Chunks Type float32 numpy.ndarray - ch4vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- ch4 volume mixing ratio
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - co2vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- co2 volume mixing ratio
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - date(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type |S8 numpy.ndarray - datesec(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 3.91 kB 4 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray - f11vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - f12vmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- f12 volume mixing ratio
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - gw(time, lat)float64dask.array<chunksize=(1, 192), meta=np.ndarray>
- long_name :
- gauss weights
Array Chunk Bytes 1.50 MB 1.54 kB Shape (977, 192) (1, 192) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - hyai(time, ilev)float64dask.array<chunksize=(1, 27), meta=np.ndarray>
- long_name :
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- long_name :
- hybrid A coefficient at layer midpoints
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- long_name :
- hybrid B coefficient at layer interfaces
Array Chunk Bytes 211.03 kB 216 B Shape (977, 27) (1, 27) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - hybm(time, lev)float64dask.array<chunksize=(1, 26), meta=np.ndarray>
- long_name :
- hybrid B coefficient at layer midpoints
Array Chunk Bytes 203.22 kB 208 B Shape (977, 26) (1, 26) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - mdt(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - n2ovmr(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- n2o volume mixing ratio
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - nbdate(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- base date (YYYYMMDD)
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - nbsec(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- seconds of base date
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - ndbase(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- base day
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - ndcur(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current day (from base day)
Array Chunk Bytes 3.91 kB 4 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray - nlon(time, lat)int32dask.array<chunksize=(1, 192), meta=np.ndarray>
- long_name :
- number of longitudes
Array Chunk Bytes 750.34 kB 768 B Shape (977, 192) (1, 192) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray - nsbase(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- seconds of base day
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - nscur(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current seconds of current day
Array Chunk Bytes 3.91 kB 4 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray - nsteph(time)int32dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- current timestep
Array Chunk Bytes 3.91 kB 4 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray - ntrk(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- spectral truncation parameter K
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - ntrm(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- spectral truncation parameter M
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - ntrn(time)int32dask.array<chunksize=(977,), meta=np.ndarray>
- long_name :
- spectral truncation parameter N
Array Chunk Bytes 3.91 kB 3.91 kB Shape (977,) (977,) Count 2 Tasks 1 Chunks Type int32 numpy.ndarray - sol_tsi(time)float64dask.array<chunksize=(1,), meta=np.ndarray>
- long_name :
- total solar irradiance
- units :
- W/m2
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - time_bnds(time, nbnd)objectdask.array<chunksize=(977, 2), meta=np.ndarray>
- long_name :
- time interval endpoints
Array Chunk Bytes 15.63 kB 15.63 kB Shape (977, 2) (977, 2) Count 2 Tasks 1 Chunks Type object numpy.ndarray - time_written(time)|S8dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 7.82 kB 8 B Shape (977,) (1,) Count 978 Tasks 977 Chunks Type |S8 numpy.ndarray - w_stag(time, slat)float64dask.array<chunksize=(1, 191), meta=np.ndarray>
- long_name :
- staggered latitude weights
Array Chunk Bytes 1.49 MB 1.53 kB Shape (977, 191) (1, 191) Count 978 Tasks 977 Chunks Type float64 numpy.ndarray - wnummax(time, lat)int32dask.array<chunksize=(1, 192), meta=np.ndarray>
- long_name :
- cutoff Fourier wavenumber
Array Chunk Bytes 750.34 kB 768 B Shape (977, 192) (1, 192) Count 978 Tasks 977 Chunks Type int32 numpy.ndarray
- Conventions :
- CF-1.0
- Version :
- $Name$
- case :
- f09_g16.B.CONTROL03
- host :
- n716024
- initial_file :
- f09_g16.B.04.EXT.cam.i.2075-01-01-00000.nc
- logname :
- clmfrm01
- revision_Id :
- $Id$
- source :
- CAM
- title :
- UNSET
- topography_file :
- /d/04/clmfrm/din/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc
Take a look through the variables, dimensions, and coordinates within the xarray dataset rendered above. The .variables field contains a list of all the data variables, which are indexed along the dimensions listed above (at the locations given by the .coordinates field). Under the covers, this data is housed in dask arrays, which is a data format well suited for parallel computing. If you click on the funky cylindrical icon to the right of one of the "Data variables", you will see a graphical representation of the underlying data chunks.
If you click on the folded paper icon, you'll see the variable metadata. Looking at FSNT and FSLT, we have "net longwave flux at top of model" and "net shortwave flux at top of model". As Professor Rose explains in his textbook, FLNT is oriented outward while FLST is oriented inward so that these quantities are positive. I believe we want the sum of these variables for our integrated net flux assessment.
Because it is tedious to expand the metadata icons above by hand, the following code cell compiles them into a denser list for convenient reading. Take a look through and verify that no other variables fit the bill better. If they do, we can plot them instead.
In [8]:
#list all variables for the "CONTROL" case and their long names
short_names = []
long_names = []
for k in list(cases["CONTROL"].variables):
try:
short_names.append(k)
long_name = cases["CONTROL"][k].long_name + " [" + cases["CONTROL"][k].units + "]"
long_names.append(long_name)
except:
continue
menu = list(tuple(zip(short_names, long_names)))
menu
Out[8]:
[('AEROD_v', 'Total Aerosol Optical Depth in visible band [1]'),
('CLDHGH', 'Vertically-integrated high cloud [fraction]'),
('CLDICE', 'Grid box averaged cloud ice amount [kg/kg]'),
('CLDLIQ', 'Grid box averaged cloud liquid amount [kg/kg]'),
('CLDLOW', 'Vertically-integrated low cloud [fraction]'),
('CLDMED', 'Vertically-integrated mid-level cloud [fraction]'),
('CLDTOT', 'Vertically-integrated total cloud [fraction]'),
('CLOUD', 'Cloud fraction [fraction]'),
('CONCLD', 'Convective cloud cover [fraction]'),
('DCQ', 'Q tendency due to moist processes [kg/kg/s]'),
('DTCOND', 'T tendency - moist processes [K/s]'),
('DTV', 'T vertical diffusion [K/s]'),
('EMIS', 'cloud emissivity [1]'),
('FICE', 'Fractional ice content within cloud [fraction]'),
('FLDS', 'Downwelling longwave flux at surface [W/m2]'),
('FLDSC', 'Clearsky downwelling longwave flux at surface [W/m2]'),
('FLNS', 'Net longwave flux at surface [W/m2]'),
('FLNSC', 'Clearsky net longwave flux at surface [W/m2]'),
('FLNT', 'Net longwave flux at top of model [W/m2]'),
('FLNTC', 'Clearsky net longwave flux at top of model [W/m2]'),
('FLUT', 'Upwelling longwave flux at top of model [W/m2]'),
('FLUTC', 'Clearsky upwelling longwave flux at top of model [W/m2]'),
('FSDS', 'Downwelling solar flux at surface [W/m2]'),
('FSDSC', 'Clearsky downwelling solar flux at surface [W/m2]'),
('FSDTOA', 'Downwelling solar flux at top of atmosphere [W/m2]'),
('FSNS', 'Net solar flux at surface [W/m2]'),
('FSNSC', 'Clearsky net solar flux at surface [W/m2]'),
('FSNT', 'Net solar flux at top of model [W/m2]'),
('FSNTC', 'Clearsky net solar flux at top of model [W/m2]'),
('FSNTOA', 'Net solar flux at top of atmosphere [W/m2]'),
('FSNTOAC', 'Clearsky net solar flux at top of atmosphere [W/m2]'),
('FSUTOA', 'Upwelling solar flux at top of atmosphere [W/m2]'),
('ICEFRAC', 'Fraction of sfc area covered by sea-ice [fraction]'),
('ICIMR', 'Prognostic in-cloud ice mixing ratio [kg/kg]'),
('ICWMR', 'Prognostic in-cloud water mixing ratio [kg/kg]'),
('LANDFRAC', 'Fraction of sfc area covered by land [fraction]'),
('LHFLX', 'Surface latent heat flux [W/m2]'),
('LWCF', 'Longwave cloud forcing [W/m2]'),
('OCNFRAC', 'Fraction of sfc area covered by ocean [fraction]'),
('OMEGA', 'Vertical velocity (pressure) [Pa/s]'),
('OMEGAT', 'Vertical heat flux [K Pa/s]'),
('P0', 'reference pressure [Pa]'),
('PBLH', 'PBL height [m]'),
('PHIS', 'Surface geopotential [m2/s2]'),
('PRECC', 'Convective precipitation rate (liq + ice) [m/s]'),
('PRECL', 'Large-scale (stable) precipitation rate (liq + ice) [m/s]'),
('PRECSC', 'Convective snow rate (water equivalent) [m/s]'),
('PRECSL', 'Large-scale (stable) snow rate (water equivalent) [m/s]'),
('PS', 'Surface pressure [Pa]'),
('PSL', 'Sea level pressure [Pa]'),
('Q', 'Specific humidity [kg/kg]'),
('QFLX', 'Surface water flux [kg/m2/s]'),
('QREFHT', 'Reference height humidity [kg/kg]'),
('QRL', 'Longwave heating rate [K/s]'),
('QRS', 'Solar heating rate [K/s]'),
('RELHUM', 'Relative humidity [percent]'),
('SFCLDICE', 'CLDICE surface flux [kg/m2/s]'),
('SFCLDLIQ', 'CLDLIQ surface flux [kg/m2/s]'),
('SHFLX', 'Surface sensible heat flux [W/m2]'),
('SNOWHICE', 'Snow depth over ice [m]'),
('SNOWHLND', 'Water equivalent snow depth [m]'),
('SOLIN', 'Solar insolation [W/m2]'),
('SWCF', 'Shortwave cloud forcing [W/m2]'),
('T', 'Temperature [K]'),
('TAUX', 'Zonal surface stress [N/m2]'),
('TAUY', 'Meridional surface stress [N/m2]'),
('TGCLDCWP', 'Total grid-box cloud water path (liquid and ice) [gram/m2]'),
('TGCLDIWP', 'Total grid-box cloud ice water path [gram/m2]'),
('TGCLDLWP', 'Total grid-box cloud liquid water path [gram/m2]'),
('TMQ', 'Total (vertically integrated) precipitable water [kg/m2]'),
('TREFHT', 'Reference height temperature [K]'),
('TS', 'Surface temperature (radiative) [K]'),
('TSMN', 'Minimum surface temperature over output period [K]'),
('TSMX', 'Maximum surface temperature over output period [K]'),
('U', 'Zonal wind [m/s]'),
('U10', '10m wind speed [m/s]'),
('UU', 'Zonal velocity squared [m2/s2]'),
('V', 'Meridional wind [m/s]'),
('VD01', 'Vertical diffusion of Q [kg/kg/s]'),
('VQ', 'Meridional water transport [m/skg/kg]'),
('VT', 'Meridional heat transport [K m/s]'),
('VU', 'Meridional flux of zonal momentum [m2/s2]'),
('VV', 'Meridional velocity squared [m2/s2]'),
('Z3', 'Geopotential Height (above sea level) [m]'),
('ch4vmr', 'hybrid level at interfaces (1000*(A+B)) [level]'),
('co2vmr', 'latitude [degrees_north]'),
('date', 'hybrid level at midpoints (1000*(A+B)) [level]'),
('date_written', 'longitude [degrees_east]'),
('datesec', 'timestep [s]'),
('f11vmr', 'staggered latitude [degrees_north]'),
('f12vmr', 'staggered longitude [degrees_east]'),
('gw', 'total solar irradiance [W/m2]')]
Right now, the data is stored in a dictionary of nine Datasets, each of which contains all 114-odd variables. Each single variable (such as 'T', or 'PRECC' or 'FLNT') is stored in an xarray DataArray. We only want the DataArrays associated with our variables of interest, and we want to be able to easily average out the Arctic Oscillation.
Let's extract the DataArrays corresponding to each relevant variable. We'll do that by defining a function which extracts the Control, Global, and Fram DataArrays for a given variable. Inside of that function will be another helper function that produces a single-intervention DataArray containing an AO-phase dimension.
In [32]:
def get_ao_indexable_vars(var):
""" Generate three DataArrays (Control, Global, and Fram) composed of the relevant variable from each case
within 'cases', one for each intervention and indexable by arctic oscillation.
Args:
var (str): the variable key, such as "FLNT"
"""
def get_ao_indexable_var(var, case_set):
""" Generate one DataArray composed of the relevant variable for one intervention, with keys given by
'case_set', indexable by arctic oscillation.
Args:
var (str): the variable key, such as "FLNT"
case_set (list of str): a set of keys for the dictionary 'cases' defined above
"""
ao_phases = ["Neutral", "Negative", "Positive"]
das = [cases[c][var] for c in case_set]
return xr.concat(das, dim=pd.Index(ao_phases, name='ao_phase', dtype='U15')) # index order matters.
control_cases = ["CONTROL", "CONTROL_MAY_NAO", "CONTROL_MAY_PAO"] # neutral, negative, positive.
control_da = get_ao_indexable_var(var, control_cases)
global_cases = ["GLOBAL", "GLOBAL_MAY_NAO", "GLOBAL_MAY_PAO"]
global_da = get_ao_indexable_var(var, global_cases)
fram_cases = ["FRAM_MAY", "FRAM_MAY_NAO", "FRAM_MAY_PAO"]
fram_da = get_ao_indexable_var(var, fram_cases)
return control_da, global_da, fram_da
flnt_control, flnt_global, flnt_fram = get_ao_indexable_vars("FLNT")
fsnt_control, fsnt_global, fsnt_fram = get_ao_indexable_vars("FSNT")
Let's load this data into memory before we take the AO-phase mean. Because xarray is lazily evaluated, we could simply never explicitly call load() if we wanted and the required data would be fetched as needed. But I've found that explicitly loading the data before conducting any operations tends to reduce the Dask scheduler pipeline bottleneck associated with fetching the data from S3 just-in-time for an operation.
While the cell below is loading, try pulling up localhost:8787 and seeing if the Dask Distributed scheduler dashboard is showing you information about the task that's completing. It's interesting to see graphical representations of the function calls, progress, and utilized memory.
I've also added some "Jupyter magic" in the first line of the cell below. The command %%time causes the iPython kernel Jupyter is using to record the time the cell took to execute. It took me about four minutes.
In [65]:
%%time
i = 1
# I created a dictionary out of these variables so they can be iteratively modified in-place
# rather than modifying copies local to the for loop.
des_vars = [flnt_control, flnt_global, flnt_fram, fsnt_control, fsnt_global, fsnt_fram]
des_vars = dict(zip(range(len(des_vars)), des_vars))
for da in des_vars.values():
print("Loading data: {}/{}".format(i, len(des_vars)))
i += 1
da.load()
Loading data: 1/6 Loading data: 2/6 Loading data: 3/6 Loading data: 4/6 Loading data: 5/6 Loading data: 6/6 CPU times: user 601 µs, sys: 23 µs, total: 624 µs Wall time: 515 µs
Now let's create the "net flux" variable we've discussed before. We will define "net" to be the sum of longwave and shortwave net fluxes. Since shortwave flux is oriented inward ( > 0 if downward) while longwave flux is oriented outward ( > 0 if upward), we subtract FSNT from FLNT and adopt the convention that our positive "net" variable corresponds to positive upwelling.
In [81]:
net_control = (flnt_control - fsnt_control)
net_global = (flnt_global - fsnt_global)
net_fram = (flnt_fram - fsnt_fram)
net_control
Taking a page from Professor Rose, we'll use the .gw variable provided in one of the Datasets as our weight factor. I had to convince myself that the mean-division performed below produces a valid multiplicative weight, i.e.
$$\sum_1^n x_i = 1.$$
In [83]:
weight_factor = cases["CONTROL"].isel(time=0).gw / cases["CONTROL"].isel(time=0).gw.mean(dim='lat')
weight_factor
Out[83]:
xarray.DataArray
'gw'
- lat: 192
- dask.array<chunksize=(192,), meta=np.ndarray>
Array Chunk Bytes 1.54 kB 1.54 kB Shape (192,) (192,) Count 983 Tasks 1 Chunks Type float64 numpy.ndarray - lat(lat)float64-90.0 -89.06 -88.12 ... 89.06 90.0
- long_name :
- latitude
- units :
- degrees_north
array([-90. , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958, -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508, -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058, -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607, -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157, -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707, -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257, -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806, -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356, -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906, -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455, -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005, -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555, -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105, -10.837696, -9.895288, -8.95288 , -8.010471, -7.068063, -6.125654, -5.183246, -4.240838, -3.298429, -2.356021, -1.413613, -0.471204, 0.471204, 1.413613, 2.356021, 3.298429, 4.240838, 5.183246, 6.125654, 7.068063, 8.010471, 8.95288 , 9.895288, 10.837696, 11.780105, 12.722513, 13.664921, 14.60733 , 15.549738, 16.492147, 17.434555, 18.376963, 19.319372, 20.26178 , 21.204188, 22.146597, 23.089005, 24.031414, 24.973822, 25.91623 , 26.858639, 27.801047, 28.743455, 29.685864, 30.628272, 31.570681, 32.513089, 33.455497, 34.397906, 35.340314, 36.282723, 37.225131, 38.167539, 39.109948, 40.052356, 40.994764, 41.937173, 42.879581, 43.82199 , 44.764398, 45.706806, 46.649215, 47.591623, 48.534031, 49.47644 , 50.418848, 51.361257, 52.303665, 53.246073, 54.188482, 55.13089 , 56.073298, 57.015707, 57.958115, 58.900524, 59.842932, 60.78534 , 61.727749, 62.670157, 63.612565, 64.554974, 65.497382, 66.439791, 67.382199, 68.324607, 69.267016, 70.209424, 71.151832, 72.094241, 73.036649, 73.979058, 74.921466, 75.863874, 76.806283, 77.748691, 78.691099, 79.633508, 80.575916, 81.518325, 82.460733, 83.403141, 84.34555 , 85.287958, 86.230366, 87.172775, 88.115183, 89.057592, 90. ]) - time()object2000-02-01 00:00:00
- bounds :
- time_bnds
- long_name :
- time
array(cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0), dtype=object)
Now we conduct the latitudinal weighting and take the mean through latitude, longitude, and AO phase. These mean operations should commute, right?
In [84]:
net_control_timeseries = (weight_factor * net_control).mean(dim=['lat', 'lon', 'ao_phase']).load()
net_global_timeseries = (weight_factor * net_global).mean(dim=['lat', 'lon', 'ao_phase']).load()
net_fram_timeseries = (weight_factor * net_fram).mean(dim=['lat', 'lon', 'ao_phase']).load()
Now we plot our generated flux variables.
In [108]:
%%output size=200
plot = hv.Curve(net_control_timeseries, label="Control") \
* hv.Curve(net_global_timeseries, label="Global") \
* hv.Curve(net_fram_timeseries, label="Fram")
plot.opts(ylabel="Net Flux [W/m^2]", title='Mean Net Flux')
WARNING:param.CurvePlot08983: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot08994: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot09005: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
Out[108]:
We see that there is high seasonal (annual) variation in mean net flux on the order of 15 W/m^2, with a minimum in February and a max in August. Maybe we've made a mistake -- it seems like integrating over the entire globe ought be more constant as winter in the southern hemisphere corresponds to summer in the northern hemisphere. On the other hand, maybe seasonal variation is attributed to different albedo distributions in each hemisphere. For instance, if the northern hemisphere has higher albedo than the southern hemisphere, we would expect to see higher net flux in August. I'm not sure why that would cause the mean net flux to change signs though ...
Let's see what the plot looks like with a low pass filter. Let's take a five-year rolling average.
In [103]:
filtered_net_control_timeseries = net_control_timeseries.rolling(time=60, center=True).mean()
filtered_net_global_timeseries = net_global_timeseries.rolling(time=60, center=True).mean()
filtered_net_fram_timeseries = net_fram_timeseries.rolling(time=60, center=True).mean()
In [109]:
%%output size=200
plot = hv.Curve(filtered_net_control_timeseries, label="Control") \
* hv.Curve(filtered_net_global_timeseries, label="Global") \
* hv.Curve(filtered_net_fram_timeseries, label="Fram")
plot.opts(ylabel="Net Flux [W/m^2]", title='Mean Net Flux, 60-month Rolling Average')
WARNING:param.CurvePlot09374: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot09385: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend. WARNING:param.CurvePlot09396: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
Out[109]:
That's interesting! Global resulted in a net mean flux that was less strongly negative than Fram or Control. Recall that Net is positive out, so these results are intuitive: on net, less radiation is remaining in Earth's system.
In [ ]: